# import the necessary packages
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2


def midpoint(ptA, ptB):
    return (ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to the input image")
ap.add_argument("-w", "--width", type=float, required=True,
                help="width of the left-most object in the image (in inches)")
args = vars(ap.parse_args())

# load the image, convert it to grayscale, and blur it slightly 图像预处理
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)

# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges #  canny边缘检测器+膨胀和腐蚀
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)

# find contours in the edge map 寻找轮廓
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

# sort the contours from left-to-right and initialize the
# 'pixels per metric' calibration variable 初始化pixelsPerMetric
(cnts, _) = contours.sort_contours(cnts)
pixelsPerMetric = None

# loop over the contours individually
for c in cnts:
    # if the contour is not sufficiently large, ignore it 太小的忽略
    if cv2.contourArea(c) < 100:
        continue

    # compute the rotated bounding box of the contour 计算图像的旋转边界框
    orig = image.copy()
    box = cv2.minAreaRect(c)
    box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
    box = np.array(box, dtype="int")

    # order the points in the contour such that they appear
    # in top-left, top-right, bottom-right, and bottom-left
    # order, then draw the outline of the rotated bounding
    # box 我们在左上方、右上方、右下方和左下方的顺序排列我们旋转的边界框坐标
    box = perspective.order_points(box)
    # 绿色绘制物体的轮廓
    cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)

    # loop over the original points and draw them ，然后将边界框矩形的顶点绘制成红色的小圆圈。
    for (x, y) in box:
        cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)

    # unpack the ordered bounding box, then compute the midpoint
    # between the top-left and top-right coordinates, followed by
    # the midpoint between bottom-left and bottom-right coordinates 计算出一系列的中点
    (tl, tr, br, bl) = box
    (tltrX, tltrY) = midpoint(tl, tr)
    (blbrX, blbrY) = midpoint(bl, br)

    # compute the midpoint between the top-left and top-right points,
    # followed by the midpoint between the top-righ and bottom-right
    (tlblX, tlblY) = midpoint(tl, bl)
    (trbrX, trbrY) = midpoint(tr, br)

    # draw the midpoints on the image 绘制蓝色中间点
    cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)

    # draw lines between the midpoints，然后将中间点与紫色线连接
    cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
             (255, 0, 255), 2)
    cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
             (255, 0, 255), 2)

    # compute the Euclidean distance between the midpoints
    dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
    dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))

    # if the pixels per metric has not been initialized, then
    # compute it as the ratio of pixels to supplied metric
    # (in this case, inches)
    if pixelsPerMetric is None:
        pixelsPerMetric = dB / args["width"]

    # compute the size of the object
    dimA = dA / pixelsPerMetric
    dimB = dB / pixelsPerMetric

    # draw the object sizes on the image
    cv2.putText(orig, "{:.1f}cm".format(dimA),
                (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,
                0.65, (255, 255, 255), 2)
    cv2.putText(orig, "{:.1f}cm".format(dimB),
                (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
                0.65, (255, 255, 255), 2)

    # show the output image
    cv2.imshow("Image", orig)
    cv2.waitKey(0)